Goodness-of-fit testing strategies from indirect observations

J.M. Loubes and C. Marteau

Journal of Nonparametric Statistics, 2014, vol. 26, issue 1, 85-99

Abstract: We consider in this paper a goodness-of-fit testing problem in a density framework. In particular, we deal with an error-in-variables model where each new incoming observation is gathered with a random independent error. It is well known that in such a situation, we are faced with an inverse (deconvolution) problem. Nevertheless, following recent results in the Gaussian white noise model, we prove that using procedures containing a deconvolution step is not always necessary.

Date: 2014
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DOI: 10.1080/10485252.2013.827680

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